Determining Postural Stability

ABSTRACT

A method for determining postural stability of a person can include acquiring a plurality of pressure data points over a period of time from at least one pressure sensor. The method can also include the step of identifying a postural state for each pressure data point to generate a plurality of postural states. The method can include the step of determining a postural state of the person at a point in time based on at least the plurality of postural states.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of, claims the benefit of andpriority to U.S. patent application Ser. No. 12/323,912 filed Nov. 26,2008, which claims the benefit of and priority to U.S. provisionalpatent Application No. 60/990,817 filed Nov. 28, 2007, the contents ofwhich are incorporated herein by reference in their entirety.

GOVERNMENT SUPPORT

This invention was made with government support under grant numbersNCC9-58-3, NNJ06HG25A, and NCC9-142, awarded by NASA. The government hascertain rights in this invention.

FIELD OF THE INVENTION

The invention generally relates to determining postural stability.Specifically, the invention relates to a method and apparatus/system fordetermining a subject's postural state.

BACKGROUND OF THE INVENTION

Poor posture can lead to postural instability (e.g., lack of balance),for example, as a person ages and/or when the person is injured. Othercauses for postural instability can include the return of a person froma zero gravity environment, a lack of exercise, and/or an injury.Detection and correction of a subject's (e.g., a person's) posturalinstability can be challenging outside of a lab environment and/or on areal-time basis.

SUMMARY OF THE INVENTION

In one aspect, the invention features a method for determining posturalstability of a person and can include the step of acquiring a pluralityof pressure data points (e.g., pressure information) over a period oftime from at least one pressure sensor. The method can include the stepof identifying a postural state for each pressure data point to generatea plurality of postural states. The method can also include the step ofdetermining a postural state of the person at a point in time based onat least the plurality of postural states.

In another aspect, the invention features a method for determining apostural stability (e.g., of a person). The method can include the stepof acquiring at least a first pressure data point and a second pressuredata point from at least one pressure sensor. The method can alsoinclude the step of identifying a first postural state and a secondpostural state based on the first and second pressure data points. Themethod can also include the step of determining a postural state (e.g.,postural state of the person) at a point in time based on at least thefirst postural state and the second postural state.

In yet another aspect, the invention features a system for determining apostural stability of a person. The system can include at least onepressure sensor coupled to the person that acquires a plurality ofpressure data points over a period of time. The system can also includea means for identifying a postural state for each pressure data pointand a means for generating a plurality of postural states of the personover the period of time. The system can also include a means fordetermining a postural state of the person at a point in time based onthe plurality of postural states.

In one aspect, the invention features a method to determine posturalstability. The method can include receiving pressure information (e.g.,pressure data points) from a sensor coupled to a load bearing structure.A current postural state for a structure associated with the loadbearing structure can be determined based on the received pressureinformation. A next postural state of the structure can be determinedbased on a range of postural stability, the current postural state, aprobability of the next postural state or any combination thereof.

In another aspect, the invention features a computer program product todetermine postural stability. The computer program product can betangibly embodied in a computer or a removable storage device. Thecomputer program product can include instructions being operable tocause a data processing apparatus to receive pressure information from asensor coupled to a load bearing structure. A current postural state fora structure associated with the load bearing structure can be determinedbased on the received pressure information. A next postural state of thestructure can be determined based on a range of postural stability, thecurrent postural state, a probability of the next postural state or anycombination thereof.

In yet another aspect, the invention features a system for determiningpostural stability. The system includes a stability processing module.The stability processing module can be configured to receive pressureinformation from a sensor coupled to a load bearing structure. Thestability processing module can be further configured to determine acurrent postural state for a structure associated with the load bearingstructure based on the received pressure information. The stabilityprocessing module can be further configured to determine a next posturalstate of the structure based on a range of postural stability, thecurrent postural state, a probability of the next postural state, or anycombination thereof.

In another aspect, the invention features a system to determine posturalstability. The system includes a means for receiving pressureinformation from a sensor coupled to a load bearing structure. Thesystem further includes a means for determining a current postural statefor a structure associated with the load bearing structure based on thereceived pressure information. The system further includes a means fordetermining a next postural state of the structure based on a range ofpostural stability, the current postural state, a probability of thenext postural state, or any combination thereof.

In other examples, any of the aspects above, or any apparatus or methoddescribed herein, can include one or more of the following features.

A range of postural stability states can include or be determined by theplurality of pressure data points. In some embodiments, a postural stateof the person is at least one of a static postural state or a dynamicpostural state. A dynamic postural state can be defined as when theperson is moving from a first static postural state to a second staticpostural state. A person can be identified or determined to beposturally stable or posturally unstable based on a number of times theperson is in the dynamic postural state.

In some embodiments, a plurality of postural states follows a punctuatedequilibrium where a continuous series of static postural states definesan equilibrium. In some embodiments, a person can be identified ordetermined to be posturally stable or unstable based on a number ofdistinct equilibria.

Determining a postural state of the person at the point in time can bebased on, at least, a probability of transitioning between the staticpostural state and the dynamic postural state. In some embodiments, theprobability of transitioning between the static postural state and thedynamic postural state can be calculated based on the plurality ofpostural states of the person over the period of time (e.g., by lookingat the trend of how the plurality of postural states varies between, forexample, static or dynamic postural states).

In some embodiments, an acceleration of the person can be acquired overthe period of time. A location of the person can also bedetermined/acquired by a sensor (e.g., GPS location device).

In some embodiments, a postural state of a person at a point in time canbe determined by using a machine learning technique (e.g., a HiddenMarkov Model or Bayesian segmentation) on the plurality of pressure datapoints to generate the plurality of postural states. A postural state ofa person at a point in time can be determined by using a Hidden MarkovModel on the plurality of pressure data points to determine a posturalstate of a person at a point in time (e.g., based on pressure points andinformation acquired to generate a plurality of postural states). AHidden Markov Model can be used on the plurality of pressure data pointsto, for example, project or calculate a postural state of a person at alater point in time. A postural state of a person at a point in time(e.g., a future/subsequent postural state) can be determined by applyingBayesian segmentation to the plurality of pressure data points (e.g.,which can be used to generate the plurality of postural states).

In some embodiments, a plurality of pressure data points are acquiredfrom at least one pressure sensor on a shoe worn by the person, a sock,a sole insert, a cane, a crutch, a walker, a walking aid used by aperson, a prosthetic leg, a robotic leg, a vehicle, or an axle connectedto at least one wheel.

Each of the plurality of pressure data points can reflect a location ofa center of mass or a center of gravity/force of the person at a pointin time. A change of the location of the center of mass of the personcan be determined over the period of time. In some embodiments,identifying a pressure data point as corresponding to a static ordynamic postural state can be done by grouping a selected set of theplurality of pressure data points as corresponding to a static posturalstate, based on the location of the center of mass/gravity/force of theperson of each of the plurality of pressure data points.

In some embodiments, determining a postural state of a person at a pointin time can include determining a subsequent postural state of theperson. A postural state of the person can be determined either in realtime or at a later point in time after the pressure points/posturalstates have been acquired/determined/identified. For example, as theplurality of pressure data points are being acquired, a postural stateof a person at a point in time (e.g., a subsequent postural state of theperson) can be determined in real time, based on the plurality ofpressure data points. In some embodiments, a postural state of a personat a point in time (e.g., a current postural state or a subsequentpostural state of the person) can be determined based on the pluralityof pressure data points which were acquired during a previous period intime (e.g., a current or subsequent postural state of a person can bedetermined based on pressure data points/postural states that wereacquired/determined previously, such as, for example, 6 months ago).

In some embodiments, a postural state of a person at a point in time caninclude determining a subsequent postural state of the person based aprobability of transitioning between the static postural state and thedynamic postural state. The probability of transitioning between thestatic postural state and the dynamic postural state can be based, atleast in part, by the identified first postural state and the identifiedsecond postural state.

In some embodiments, a person can be identified (e.g., determined to be)posturally stable or posturally unstable based on at least an identifiedfirst postural state and an identified second postural state (e.g.,identified from pressure data points).

In some embodiments, a Hidden Markov Model calculation is utilized todetermine a next postural state. Probabilities of transitioning betweena plurality of stable and unstable postural states can be utilized todetermine the next postural state. In some embodiments, theprobabilities of transitioning between stable and unstable posturalstates are determined. The probabilities of transitioning between stableand unstable postural states can be determined based on the range ofpostural stability.

In some embodiments, the received pressure information is stored. Therange of postural stability for a structure can be determined based onthe stored pressure information. In some embodiments, the currentpostural state is stored. A statistic, a score, and/or a simulation isdetermined based on the stored postural state. A message is transmittedto a postural analysis module using a network. The message includes thestatistic, the score, the simulation, the current postural state, thenext postural state, and/or the range of postural stability.

In some embodiments, the current postural state includes activityinformation, performance information, fatigue information, and/ordiagnosis information. The range of postural stability can be unique forthe structure.

In some embodiments, the structure is a human and the load bearingstructure is the human's lower extremities. The structure can be a humanand the load bearing structure can be a cane, a crutch, a walker, aprosthetic leg, a walking aid, or any combination thereof. The structurecan be a robotic device or a vehicle and the load bearing structure canbe a leg and/or an axle coupled to one or more wheels.

In some embodiments, the pressure information is received from aplurality of sensors which are coupled to the load bearing structure. Amessage can be transmitted to the structure. The message can relate tothe next postural state. In some embodiments, a message is transmittedto a stability management module. The message can include supplementalsensory information to stimulate the structure to modify the nextpostural state from unstable to stable. In some embodiments, a messageis transmitted to a stability management module. The message can includeweight adjustment information to modify the next postural state fromunstable to stable.

In some embodiments, a safety device is activated based on the currentpostural state and/or the next postural state. A message can betransmitted to a communication module using a network. The message caninclude the current postural state, the next postural state, and/or therange of postural stability.

The system can also include a handheld portable stability device. Thehandheld portable stability device can include the stability processingmodule. In some embodiments, the system further includes a stabilitymanagement module configured to adjust a weight device to modify thenext postural state from unstable to stable. In some embodiments, thesystem further includes a safety device configured to activate based onthe current postural state and/or the next postural state. The sensorcan be positioned in or disposed relative to a sock, a shoe, a soleinsert, a cane, a crutch, a walker, a walking aid, a prosthetic leg, arobotic leg, a vehicle, and/or an axle connected to one or more wheels.

Shoes can be designed with pressure sensors (e.g., pressure sensorsinside the shoes) that can be utilized to track how a person is doingand how well the person is controlling their balance throughout the day.A postural stability device can be portable and can be utilized on adaily basis while the person is conducting daily activities. A personcan be notified regarding a pending unstable postural state, which canprevent falls and/or injuries to the person. A postural stability of aperson over a period of time can be tracked and monitored, which canallow for the monitoring and/or correction of the person's dailyactivities. Tracking of a person's postural stability can allow for theassessment of medical and/or physical therapy. The determination ofpostural stability can be utilized in connection with safety devices toprotect against injuries.

Other aspects and advantages of the invention can become apparent fromthe following drawings and description, all of which illustrate theprinciples of the invention, by way of example only.

BRIEF DESCRIPTION OF THE DRAWINGS

The advantages of the invention described above, together with furtheradvantages, may be better understood by referring to the followingdescription taken in conjunction with the accompanying drawings. Thedrawings are not necessarily to scale, emphasis instead generally beingplaced upon illustrating the principles of the invention.

FIG. 1 is an exemplary diagram illustrating soles of a shoe whichinclude a stability processing module, according to an illustrativeembodiment of the invention.

FIG. 2 is an exemplary diagram illustrating a human with a handheldstability device, according to an illustrative embodiment of theinvention.

FIG. 3 is an exemplary diagram illustrating a walker with a stabilitymodule, according to an illustrative embodiment of the invention.

FIG. 4 is an exemplary diagram illustrating a person with a cane and ahandheld stability device, according to an illustrative embodiment ofthe invention.

FIG. 5 is an exemplary diagram illustrating a person playing golf with ahandheld stability device, according to an illustrative embodiment ofthe invention.

FIG. 6 is an exemplary diagram illustrating a robot with a stabilitymodule, according to an illustrative embodiment of the invention.

FIG. 7 is an exemplary flowchart illustrating the determination ofpostural states, according to an illustrative embodiment of theinvention.

FIG. 8 is an exemplary flowchart illustrating the checking of posturalstates, according to an illustrative embodiment of the invention.

FIG. 9 shows a state graph, according to an illustrative embodiment ofthe invention.

FIG. 10 is an exemplary chart illustrating equilibrium, according to anillustrative embodiment of the invention.

FIG. 11 is an exemplary chart illustrating a safe zone, according to anillustrative embodiment of the invention.

FIG. 12 is an exemplary chart illustrating equilibrium, according to anillustrative embodiment of the invention.

FIG. 13 is an exemplary chart illustrating equilibrium, according to anillustrative embodiment of the invention.

FIG. 14 is an exemplary chart illustrating equilibrium, according to anillustrative embodiment of the invention.

FIG. 15A is an exemplary illustration of data acquired from a pressuresensor, according to an illustrative embodiment of the invention.

FIG. 15B illustrates a method for determining postural stability of aperson based on the exemplary illustration of the data from FIG. 15A,according to an illustrative embodiment of the invention.

FIG. 16 is an illustrative graph comparing the number of equilibria fromsubjects.

DETAILED DESCRIPTION OF THE INVENTION

A postural state of a person or subject can be utilized to notify theperson and/or activate a safety device. The method and system fordetermining a subject's postural state can include a pressure sensor.Pressure information is received from the pressure sensor. The pressuresensor can be coupled to, for example, the sole of a person's shoe. Thepressure information can be utilized to determine a current posturalstate of the person. A next postural state (e.g., a subsequent posturalstate) of the person can be determined using a Hidden Markov Model (HMM)calculation. The HMM calculation can utilize the current postural stateof the person, a range of postural stability associated with the person,and/or probabilities of the transitions between the current posturalstate and the next postural state. The next postural state can beutilized to take corrective action to change the next postural statefrom unstable to stable (e.g., weight management device utilized toredistribute weight, stimulate the feet of the person), to activate asafety device (e.g., airbag, inflatable under garments), and/or tonotify the person regarding the next postural state (e.g., “Warning—YouMay Fall!,” “Sit Down Immediately!”).

The illustrative embodiments as described herein can be utilized, forexample, by physical therapists, doctors, athletes, astronauts,patients, and/or any person that needs to assess and/or correct posturalstability. The illustrative embodiments as described herein can, forexample, quantify the vestibular and other sensory feedback systems ofthe body which are used to maintain balance during quiescent standing,locomotion (e.g., running, walking), and/or other sensory-motoractivities (e.g., dancing, kneeling). For example, a physical therapistcan utilize the postural states to gauge the progress of a strokepatient as the patient relearns skills such as standing, walking, and/orrunning. As another example, the postural states could be utilized tohelp train athletes by quantifying their daily behavior (e.g., timespent running, time spent sitting) during training.

FIG. 1 is an exemplary diagram 100 illustrating soles 110 a and 110 b(generally 110) of a shoe which can include a stability processingmodule 150, input sensors 120 a and 120 b (generally 120), outputmodules 130 a and 130 b (generally 130), and a communication module 140,according to an illustrative embodiment of the invention. The inputsensors 120 measure sensor information (e.g., pressure information). Thesensor information from the input sensors 120 b can be transmitted tothe communication module 140. The transmission from the input sensors120 b to the communication module 140 can be, for example, via a wireembedded into the sole 110 b. The communication module 140 can transmitthe sensor information from the input sensors 120 b to the stabilityprocessing module 150. The transmission from the communication module140 to the stability processing module 150 can be, for example, via apersonal area network (PAN). The sensor information from the inputsensors 120 a can be transmitted to the stability processing module 150.The transmission from the input sensors 120 a to the stabilityprocessing module 150 can be, for example, via a wire embedded into thesole 110 a.

The stability processing module 150 determines the current posturalstate of the person associated with the soles 110 based on the sensorinformation. The stability processing module 150 can determine the nextpostural state of the person (e.g., a subsequent postural state of aperson) based on a range of postural stability, the current posturalstate, and/or a probability of the next postural state.

If the next postural state is determined to be unstable (e.g., fallingdown, loss of equilibrium), then the stability processing module 150 cantransmit a message to the output modules 130. The message transmitted tothe output modules 130 can include instructions to modify the nextpostural state from unstable to stable. The output modules 130 can be,for example, vibration modules which vibrate to notify the person of thepending unstable state and/or to increase blood flow to the person'sfeet. The increased blood flow to the person's feet can, for example,provide the muscular strength and/or flexibility for the person toadjust his/her postural state.

For example, the input sensors 120 are pressure sensors 120 which sensethe pressure of a person's foot in each of the soles 110. The pressureinformation sensed by the pressure sensors 120 in each of the soles 110can be transmitted to the stability processing module 150. The pressuresensors 120 a in the sole 110 a with the stability processing module 150can transmit the pressure information via a wire embedded into the sole110 a. The pressure sensors 120 b in the sole 110 a without thestability processing module 150 can transmit the pressure informationvia a wire embedded into the sole 110 b to the communication module 140.The communication module 140 can transmit the pressure informationwirelessly to the stability processing module 150.

The stability processing module 150 can determine the current posturalstate by calculating the center of force of the person utilizing thepressure information. The determination of the current postural statecan utilize the HMM calculation. The HMM calculation can utilizes a setof probabilities for each postural state to determine the next posturalstate. If the next postural state is stable, then the stabilityprocessing module 150 can continue to monitor the person. If the nextpostural state is unstable, then the stability processing module 150 canactivate the output modules 130 to vibrate which notifies the personthat they may fall and should take corrective action immediately.

In some embodiments, the soles 110 are inserts for shoes. The soles 110can be, for example, integrated into shoes. A stable equilibrium in aperson can be, for example, described as a well-controlled posture whilean unstable equilibrium can be a poorly controlled posture.

In some embodiments, the input sensors 120 are pressure sensors thatmeasure pressure information. The input sensor 120 can be, for example,a motion sensor, a temperature sensor, a heat sensor, a dielectricsensor, an electrical sensor, a magnetic sensor, a flow sensor, ahumidity sensor, a chemical sensor, a light sensor, a sound sensor,and/or any other type of sensor.

In some embodiments, the transmission of the sensor information from thecommunication module 140 to the stability processing module 150 isthrough a network. The network can be, for example, a network (e.g.,wired, wireless).

In some embodiments, the output modules 130 are electrical outputmodules which output electrical pulses and/or mechanical output moduleswhich output mechanical pulses and/or stimuli. The electrical pulsesand/or the mechanical pulses can, for example, notify the person of thenext postural state and/or can stimulate the person to modify the nextpostural state from unstable to stable. The stimulation can occur inreal-time after the stability processing module 150 determines that thenext postural state is unstable, which can prevent the person fromfalling while and also improve circulation and/or muscle tone.

In some embodiments, the stability processing module 150 transmits thenext postural state, the current postural state, the sensor information,and/or the range of postural stability to a postural analysis module(not shown). The postural analysis module can be, for example, ahandheld portable device for use by the person wearing the sensordevice, a handheld portable device for use by a person monitoring theperson wearing the sensor device, part of a computing device (e.g.,computer at doctor's office, computer at the person's home) used toautomatically monitor the person wearing the sensor device, or anycombination thereof. The transmission to the postural analysis modulecan be, for example, through a network (e.g., public switched telephonenetwork (PSTN), a local area network (LAN), a radio area network (RAN),a personal area network (PAN), the internet). The postural analysismodule can, for example, utilize the transmitted information to monitor,track, and/or notify the person regarding their postural states. Thepostural analysis module can, for example, store the transmittedinformation for historical analysis by the person being monitored and/ora third party monitoring the person (e.g., doctor, physical therapist).

In some embodiments, the stability processing module 150 transmits thecurrent postural state and/or the sensor information to the posturalanalysis module. The postural analysis module can, for example, storethe current postural state and/or the sensor information. The posturalanalysis module can, for example, determine a statistic (e.g.,percentage of time running, percentage of time sitting), a score (e.g.,number of falls per day, average number of falls per month), asimulation (e.g., with increased physical therapy will the number offalls decrease, with increased training can the athlete distributehis/her mass better), and/or any other type of metric based on thestored postural state and/or the stored sensor information. The posturalanalysis module can, for example, display the statistic, the score,and/or the simulation for use by the person being monitoring and/or thethird party. The postural analysis module can, for example, store thestatistic, the score, and/or the simulation.

In some embodiments, the postural state is stable or unstable. Thepostural state can, for example, include activity information (e.g.,walking, running, sitting), performance information (e.g., time spentwalking, time spent running), fatigue information (e.g., time spentclose to outer range of postural stability, time spent close to centerof postural stability), and/or diagnosis information (e.g., limp,lameness, neural condition, muscular condition, vision-relatedcondition).

In some embodiments, the determination of a next postural state, currentpostural state, and/or past postural states utilizes a posteriordecoding algorithm, a Bayesian segmentation, a graphical model, achoice-point method, and/or any other type of algorithm that classifiestime periods into static and/or dynamic periods. A dynamic Bayesiannetwork can be, for example, utilized to determine the next and/or pastpostural states based on the current postural state, the range ofpostural stability, and/or the probabilities of the next postural state.

In some embodiments, the determination of the next, current, and/or pastpostural states utilizes a forward algorithm, a Viterbi algorithm, aforwards-backwards algorithm, Baum-Welch algorithm, and/or any othertype of algorithm that classifies time periods into static and/ordynamic periods. The forwards-backwards algorithm can be, for example,utilized to determine the probability of the next state (e.g., dynamic,equilibrium). The Viterbi algorithm can be, for example, utilized todetermine the probability of the next state. The Baum-Welch algorithmcan be, for example, utilized to determine the range of posturalstability and/or the probabilities of transitioning between states.

In some embodiments, the HMM calculation determines the next state, thecurrent state, and/or one or more past states (e.g., five, ten). The HMMcalculation can be, for example, utilized to determine the probabilitiesof the sequence of the past states, the current state, and/or the nextstate. The sequence of the past states can be, for example, utilized tocalculate the probability of the next state. The sequence of the paststates can be, for example, utilized to determine the score, thestatistic, and/or the simulation.

Although FIG. 1 illustrates soles 110 with two input sensors 120 each,the soles 110 can have a plurality of input sensors (e.g., four, ten,twenty, etc.). In some embodiments, only one of the soles 110 has inputsensors 120. In some embodiments, there is only one input sensor 120utilized for sensor information (i.e., there is only one input sensor120 between the two soles 110).

In some embodiments, the range of postural stability is determined forthe person utilizing the soles 110. The range of postural stability canbe, for example, unique for the person since the range of posturalstability can be affected by age, activity level, postural stance,weight, medical history, and/or any other factor that can affect aperson's posture.

In some embodiments, the range of postural stability is determined basedon sensor information which is stored by the stability processing module150. The range of postural stability can be determined, for example, byprocessing the stable postural states to determine the range of stablepostural states. The determination of the range of the posturalstability can occur, for example, in real-time while the user is wearingthe shoes with the soles 110.

In some embodiments, the range of postural stability is based on aperson's center of gravity. A person's center of gravity can vary, forexample, in a range because a human can be modeled as an invertedpendulum in which an upright stance is an unstable equilibrium. Sincesmall natural center of mass deviations (e.g., breathing, limbmovements, head movement) can disrupt the equilibrium, then the pendulum(i.e., which can represent the person) can top over without appropriatesensory-motor control system. Generally, standing posture utilizessubconscious sensory feedback mechanisms (e.g., vision, tactilesensations, vestibular organs) to maintain upright stance (i.e., astable postural state). An advantage of determining the posturalstability of a person is that the person can have a real-time readout oftheir capacity to balance at any given point in time.

In some embodiments, the range of postural stability is pre-determinedfor the person based on preset parameters. For example, there can bepreset parameters based on a person's age, weight, height, activitylevel, and/or any other type of parameter associated with posture.

In some embodiments, the range of postural stability is a globaloptimum. The global optimum indicates, for example, that there is asingle optimal point for upright posture. If a person is not at theoptimum, then the person's body always directs the person towards theoptimum.

In other embodiments, the range of postural stability is a safe zone.The safe zone can be, for example, a zone of upright posture. Insidethis zone, a person is stable in regards to postural stability and aperson moves around this zone at random. Every person can, for example,have a safe zone. The safe zone for every person can be, for example,unique from other safe zones.

In some embodiments, the range of postural stability is a punctuatedequilibrium. The punctuated equilibrium can be, for example, a safe zonewith a constant turnover of equilibria. This transient equilibria form,persist, and dissipates following control failure (e.g., not inequilibrium, including falling down). Following a control failure, a newequilibria forms and control is restored.

Although FIG. 1 illustrates a person wearing a shoe, the input sensors120 can be coupled to a load bearing structure associated with astructure such that the postural states are determined for thestructure. The structure can be, for example, a vehicle and/or any othertype of structure associated with a load bearing structure. The loadbearing structure can be, for example, a leg, an axle couple to one ormore wheels, and/or any other type of structure that is load bearing. Insome embodiments, the structure is a car and the load bearing structuresare the axles coupled to the wheels, where the input sensors 120 arecoupled to the axles.

In some embodiments, the input sensors 120 are coupled to a bed toreceive input information associated with the person on the bed. Theinput sensors 120 can be, for example, coupled to a seat of a chair toreceive input information associated with the person sitting in thechair. For example, the input sensors 120 can be coupled to the seat ina car. When the stability processing module 150 determines that thecurrent postural state and/or the next postural state is the personbeing ejected from the seat (e.g., in an accident), then the stabilityprocessing module 150 can activate a safety device (e.g., air bag,anti-lock brakes).

In some embodiments, the output module 130 is coupled to the bed and/orchair. The output module 130 can be, for example, a mechanicalstimulator which can activate when the person has been sitting and/orlying for a set period of time (e.g., one minute, twenty minutes). Forexample, the stability processing module 150 is set to activate themechanical stimulator for one minute every sixty minutes of sitting.When the stability processing module 150 determines that the person hasbeen sitting for sixty minutes (e.g., continuous sitting, accumulativesitting), then the mechanical stimulator is activated for one minute. Aperson's muscles can be automatically stimulated based on inactivity toprevent muscle decay due to the inactivity.

FIG. 2 is an exemplary diagram 200 illustrating a person 210 with ahandheld stability device 240, shoes 220 a and 220 b (generally 220),and input sensors 222 a and 222 b (generally 222), according to anillustrative embodiment of the invention. In this embodiment, the person210 is wearing shoes 220. Each of the shoes 220 can have input sensors222. The input sensors 222 can receive sensor information associatedwith the person. The input sensors 222 can transmit the sensorinformation to a communication module 244 in the handheld device 240.The communication module 244 can transmit the sensor information to thestability processing module 246. The stability processing module 246 candetermine the current postural state and the next postural state. Thecurrent postural state and the next postural state can be displayed onthe display module 242.

In some embodiments, a message is transmitted from the stabilityprocessing module 246 to the display module 242. The message can bedisplayed on the display module 242. The message can include informationinforming the person of a stable equilibrium (e.g., “Great Stance!,”“Stand Straight!”), instructional information informing the person totake action to not fall (e.g., “Sit Down!,” “Warning—You may Fall!”),reporting information (e.g., “You ran two hours today,” “You have beensitting too long today.”), and/or any other type of informationcollected and/or utilized by the handheld device. The reportinginformation can include information generated and/or determined by thestability processing module 246 which can include the score, thestatistic, and/or the simulation.

In some embodiments, the communication module 244 receives sensorinformation through a wireless network (e.g., PAN, RAN) from the inputsensors 222.

In some embodiments, the display module 242 is a liquid crystal display(LCD) device which displays information (e.g., corrective information,current posture information). The display module 242 can be, forexample, a light emitting diode (LED) and/or any other type of displaywhich notifies the person 210 of the postural states. In someembodiments, the legs of the person 210 are the load bearing structuresand the person 210 is the structure.

In some embodiments, the handheld device 240 and the input sensors 222are utilized to determine the postural stability of an astronaut after aspaceflight. The changes in gravitational field strength during aspaceflight can, for example, disrupt the postural stability of anastronaut. The handheld device 240 can allow for the tracking of theastronaut's postural stability on a long-term basis without interferingwith the astronaut's daily activities. In some embodiments, the handhelddevice 240 and the input sensors 222 are utilized to monitor thepostural state of an astronaut during spaceflight and/or environments ofaltered gravity (e.g., on the moon, on mars).

In some embodiments, the handheld device 240 is a portable handhelddevice 240. The portable handheld device 240 can be utilized during aperson's daily activities and may not interfere with the collection ofsensor information and/or postural states during normal activity.

Although FIG. 2 illustrates the handheld device 240 associated with theperson 210 that is associated with the sensor information, the handhelddevice 240 can be utilized by a third party (e.g., physical therapist,doctor) to track the postural states of the person (e.g., a patient, anathlete). The third parties can track the progress of the patient as thepatient relearns skills such as standing, walking, and/or running viathe handheld device 240.

In some embodiments, the input sensors 222 communicate via a wirelessnetwork (e.g., PAN, RAN) to a remote computing device (not shown) whichis utilized by third parties (e.g., caregiver, doctor) to monitor theperson's postural stability. The remote computing device can, forexample, store the sensor information. The remote computing device can,for example, determine the statistic, the score, the simulation, and/orany other type of information based on the sensor information.

In some embodiments, the input sensors 222 are coupled to any loadbearing structure (e.g., person's lower extremities, socks) associatedwith the structure (in this example, the person).

Although FIG. 2 illustrates a person 210 with shoes 220, the person 210can be in a spacesuit and the shoes 220 can be the boots of thespacesuit. The input sensors 222 can be coupled to the boots and/or theother lower extremities of the spacesuit. The handheld device 240 can beintegrated into the other devices and/or modules of the spacesuit.

FIG. 3 is an exemplary diagram 300 illustrating a walker 310 with astability processing module 340, a stability management module 350, adisplay module 360, a speaker 365, vibrators 368, input sensors 320 a,320 b, 320 c, and 320 c (generally 320), and weight devices 355 a, 355b, 355 c, and 355 d (generally 355), according to an illustrativeembodiment of the invention. The walker can include input sensors 320which are coupled to each leg of the walker 310. The input sensors 320can receive input information and can transmit the input information tothe stability processing module 340. The stability processing module 340can determine the current postural state.

The stability processing module 340 can determine the next posturalstate based on the current postural state, the range of posturalstability, and/or the probabilities of the next postural state. If thenext postural state is unstable, then the stability processing module340 can send a message to the stability management module 350. Based onthis message, the stability management module 350 can transmitinstructions for the speaker 365 to announce a warning (e.g., “SitDown!,” “Unstable State!”), for the display module 360 to display awarning, for the vibrators 368 to vibrate, and/or for the weight devices355 to adjust to stabilize the walker 310 and/or the person. The weightdevices 355 can be coupled to each of the legs of the walker 310.

In some embodiments, the current postural state can be transmitted tothe display module 360. The display module 360 can display the currentpostural state. For example, if the current postural state is stable,then the display can be “Stable Posture.” If the current postural stateis unstable, then the display can be “Unstable—Sit Down Immediately!”

In some embodiments, the stability processing module 240 and/or thestability management module 350 are utilized to auto balance the walker310. The auto balance can utilize weight devices 355 to balance thewalker 310.

Although FIG. 3 illustrates a weight device (e.g., 355 a) coupled toeach of the legs of the walker 310, the walker 310 can have one weightdevice (e.g., 355) that could be centrally mounted on the walker 310.The centrally mounted weight device (e.g., 355 a) can be utilized tomodify the postural state of the walker 310 and/or person from unstableto stable.

FIG. 4 is an exemplary diagram 400 illustrating a person 410 with a cane430, a handheld stability device 420, and input sensors 412 a, 412 b,and 412 c (generally 412), according to an illustrative embodiment ofthe invention. The person 410 can utilize cane 430 and the person's feetas load bearing structures. The input sensors 412 can be coupled to theperson's shoes and the cane 430, respectively. The input sensors 412 cantransmit input information to the communications module 424 via anetwork (e.g., PAN, RAN). The communications module 424 can transmit theinput information to the stability processing module 422. The stabilityprocessing module 422 can determine the current postural state and thenext postural state of the person.

Although FIG. 4 illustrates the person 410 utilizing the cane 430, theperson 410 could be utilizing a crutch, a prosthetic leg, and/or anyother walking aid. The input sensors 412 can be coupled to any of theseload bearing structures.

FIG. 5 is an exemplary diagram 500 illustrating a person 510 playinggolf utilizing a golf club 512, a handheld stability device 530, astability processing module 534, a communication module 536, and inputsensors 522 a and 522 b (generally 522), according to an illustrativeembodiment of the invention. The person's shoes 510 can include inputsensors 522 which receive sensor information. The sensor information canbe transmitted to the communications module 536 via a network (e.g.,PAN, RAN). The communications module 536 can transmit the sensorinformation to the stability processing module 534.

The stability processing module 534 can determine the current posturalstate based on the sensor information. The current postural state can betransmitted to the display module 532 which displays the postural state(e.g., “Good Postural Stance!”). The stability processing module 534 canprocess a simulation to determine if any changes can be made to theperson's posture to

The stability processing module 534 can determine the next posturalstate based on the current postural state, the range of posturalstability, and/or the probability of the next postural state. Theprobability of the next postural state can be, for example, customizedaccording to sporting activity. For example, since football includesexternal forces acting on the person (i.e., one or more third partiestackling the person), then the probabilities of the next postural statecan take those external forces into account (e.g., the probability thatthe next postural state changes from running to tackled may be 0.9 for afootball player while the probability is only 0.1 for a track runner).

Although FIG. 5 illustrates the handheld postural device 530 utilized toanalyze the person's golf game, the handheld postural device 530 can beutilized for other activities (e.g., sports). For example, the handheldpostural device 530 can be utilized for track and field, Americanfootball, baseball, cricket, soccer, basketball, hockey, bowling,gymnastics, skiing, figure skating, dance and/or any other type ofsporting activity.

FIG. 6 is an exemplary diagram 600 illustrating a robot 610 with astability processing module 620, a stability management module 630,input sensors 612 a and 612 b (generally 612), and weight devices 614 a,614 b, and 614 c (generally 614), according to an illustrativeembodiment of the invention. The robot 610 can have input sensors 612.The input sensors 612 can be coupled to the load bearing structures ofthe robot 610 (i.e., the legs of the robot 610). The input sensors 612can receive input information (e.g., pressure information from each legof the robot 610). The input sensors 612 can transmit the inputinformation to the stability processing module 620. The stabilityprocessing module 620 can determine the current postural state of therobot 610 based on the input information. If the current postural stateis unstable, then the stability processing module 620 can transmit amessage to the stability management module 630. The message can includeinformation regarding the instability (e.g., all of the weight is on theleft leg, weight is not distributed evenly). Based on the message, thestability management module 630 can transmit a message to the weightdevices 614 to adjust the weight balance of the robot 610. Theseadjustments by the stability management module 630 can be used to modifythe postural state from unstable to stable.

The stability processing module 620 can determine the next posturalstate based on the current postural state, the range of posturalstability, and/or the probability of the next postural state.

In some embodiments, the range of postural stability is predeterminedfor the model of the robot 610. For example, Model R2 robots can have aset range of postural stability based on the distribution of theircomponents. In some embodiments, the range of postural stability isdetermined in real-time based on the configuration of the robot 610. Forexample, the robot 610 can determine which modules (e.g., extra battery,welder attachment, voice processor) it contains and determines the rangeof postural stability based on its included modules.

FIG. 7 is an exemplary flowchart 700 illustrating the determination ofpostural states utilizing the exemplary diagram 200 of FIG. 2, accordingto an illustrative embodiment of the invention. The communication module244 receives (710) the pressure information (e.g., pressure data points)from the sensors 222. The pressure information can be acquired over aperiod of time. The pressure information can be communicated to thestability processing module 246. The stability processing module 246 candetermine (720) the current postural state of the person 210 based onthe pressure information and store (740) the pressure information. Basedon the stored pressure information, the stability processing module 246determines (750) the range of postural stability.

The stability processing module 246 can determine (730) the nextpostural state of the person 210 (e.g., a subsequent postural state ofthe person) based on the current postural state, the range of posturalstability, and/or the probability of the next postural state. Thestability processing module 246 can transmit (760) a message whichincludes the next postural state to the display module 242 for displayto the person 210. After the stability processing module 246 determines(730) the next postural state, the processing can receive (710) the nextset of pressure information from the sensors 222.

FIG. 8 is an exemplary flowchart 800 illustrating the checking ofpostural states utilizing the exemplary diagram 600 of FIG. 6, accordingto an illustrative embodiment of the invention. The stability processingmodule 620 can receive (810) the pressure information from the sensors612. The stability processing module 620 can determine (820) the currentpostural state of the robot 610 based on the pressure information.

The stability processing module 620 can determine (830) the nextpostural state of the robot 610 based on the current postural state, therange of postural stability, and/or the probability of the next posturalstate. The stability processing module 620 can check (860) the nextpostural state. If the next postural state is stable, then theprocessing can continue by receiving (810) the next set of pressureinformation from the sensors 612. If the next postural state isunstable, then the stability processing module 620 can utilize thestability management module to adjust (870) the weight devices 614. Theadjustment (870) of the weight devices 614 can modify the next posturalstate from unstable to stable.

Although FIG. 8 illustrates the adjustment (870) of weight devices 612in unstable states, the weight devices can also be adjusted (870) tomodify the next postural state from one state to another state (e.g.,standing to sitting, dynamic to equilibrium, running to walking).

In some embodiments, the probability of transitioning between posturalstates is determined. The probabilities can be, for example, determinedbased on the stored sensor information and/or the range of posturalstability.

For example, the HMM calculation utilizes various states (e.g., currentstate, one or more past states) and the probabilities of the hiddenstates (e.g., current state, one or more past states) to determine thenext postural state. FIG. 9 depicts a state graph, according to anillustrative embodiment of the invention. By way of example, if thestate graph in FIG. 9 is utilized with tables 1 and 2, then theprobability of the next postural state can be determined. As shown inFIG. 9 for each state 905-920 there can be corresponding observations925-940. S_(i) (905), S_(i+1) (910), S_(i+2) (915), and S_(i+3) (920)represent the states and O_(i) (925), O_(i+1) (930), O_(i+2) (935), andO_(i+3) (940) represent the possible observations.

TABLE 1 Emission Probability S_(i) Equilibrium Dynamic O_(i) FastVelocity 0.2 0.7 Slow Velocity 0.8 0.3

TABLE 2 Transition Probability S_(i+1) Equilibrium Dynamic S_(i)Equilibrium 0.98 0.02 Dynamic 0.32 0.68

In some embodiments, the observation O_(i) (925) is utilized with thepast states (e.g., S_(i−1), S_(i−2), S_(i−3)) and the probabilities ofthe sequence of the past states to determine the probability of thecurrent state S_(i) (905). The probabilities of the observation O_(i)(925) in the emission probability, table 1 (e.g., which can be used todetermine an emission matrix), and the transition probability, table 2(e.g., which can be used to determine a transition matrix), can be, forexample, utilized together to determine the probability of the currentstate S_(i) and/or the next state S_(i+1). For example, if the past fourstates were equilibrium, then the probability that the current statewill stay in equilibrium, the probability that the current state willchange to dynamic, and the probability of the state associated with theobservation is utilized to determine the current state and/or the nextstate. As another example, if the sequence of the past four states isequilibrium, dynamic, equilibrium, and equilibrium, then the probabilityof these transitions in relation to each other, the probability of thecurrent state changing or staying the same (in this example,equilibrium), and the probability of the state associated with theobservation is utilized to determine the current state and/or the nextstate. The context of the transitions and/or no transitions (e.g., thelack of transitioning) between the past states is utilized to determinethe next state, thereby providing the calculation with a history.

In some embodiments, the dynamic state represents three possibleoutcomes: return to present equilibrium, transition to new equilibrium,or falling down. In some embodiments, the observations include sitting,standing, kneeling, lying down, falling, and/or any other posturalposition of a structure (e.g., person, robot). In some embodiments, theobservations (e.g., O_(i)) include any type of observation of posturalstate (e.g., falling, standing, running, walking).

In some embodiments, velocity includes the center of mass velocity forthe structure. The center of mass velocity can be, for example, measuredby the input sensors (e.g., pressure sensors). The slow velocity andfast velocity can be, for example, relative (e.g., determined bycomparing the measured center of mass velocity).

In some embodiments, the HMM calculation utilizes various states and theprobabilities of the next hidden state to determine the next posturalstate. By way of example, if the state in FIG. 9 is utilized with tables3 and 4, then the probability of the next postural state can bedetermined.

TABLE 3 Emission Probability S_(i) Dynamic Equilibrium Standing StandingWalking Running O_(i) Fast Gait and 0.15 0.01 0.04 0.60 Fast VelocityFast Gait and 0.20 0.03 0.30 0.06 Slow Velocity Slow Gait and 0.40 0.010.06 0.32 Fast Velocity Slow Gait and 0.05 0.05 0.60 0.02 Slow VelocitySlow Gait and 0.20 0.30 0.00 0.00 No Velocity No Gait and No 0.00 0.600.00 0.00 Velocity

TABLE 4 Transition Probability S_(i+1) Dynamic Equilibrium StandingStanding Walking Running S_(i) Dynamic 0.60 0.30 0.05 0.05 StandingEquilibrium 0.25 0.60 0.10 0.05 Standing Walking 0.20 0.05 0.60 0.15Running 0.12 0.10 0.18 0.60

In some embodiments, the stability processing module (e.g., 246 of FIG.2) analyzes the range of postural stability to determine the currentpostural state and/or the next postural state. If the received pressureinformation is within set parameters of the range (e.g., 25% to 75%, 10%to 90%), then the stability processing module can determine that thenext postural state is equilibrium (e.g., equilibrium running,equilibrium walking). If the received pressure information is not withinthe set parameters of the range, then the stability processing modulecan determine that the next postural state is dynamic (e.g., dynamicfalling, dynamic walking).

In some embodiments, the probabilities of two or more possible nextpostural states are the same and/or substantially similar, so the nextpostural state may not be determined. The stability processing module(e.g., 246 of FIG. 2) can process the input information received fromthe input sensors, the range of postural stability, and/or the currentpostural state to determine the next postural state.

In some embodiments, the stability processing module (e.g., 246 of FIG.2) processes the input information received from the input sensors, therange of postural stability, the current postural state, and/or the nextpostural state to determine if activity (e.g., alarm, email,notification) should be initiated based on the processing.

In some embodiments, the processing applies one or more rules todetermine if a condition occurs. By way of example, a rule can includedetermining whether the person entered a dynamic state more than tentimes in a thirty minute period. A rule can also include determining if,for example, the person has been in a dynamic state for 75% of the timeover the past twenty four hours. The rules can be, for example,predetermined (e.g., set of rules based on age, set of rules based on amedical condition) and/or automatically generated (e.g., the person isusually in equilibrium 90% of the time in a two hour period so anypercentage less than 90% in a two hour period sends an email to theperson's caregiver). The automatically generated rules can be, forexample, based on individual characteristics of the structure (e.g.,specific percentage of state over time, number of times in dynamic stateper hour), general characteristics of the structure (e.g., age range,medical condition), and/or any other metric associated with thestructure. The activity initiated can be, for example, setting off thealarm, notifying the structure, notifying the third party (e.g., sendingan email to the doctor, sending a text message to the caregiver), and/orany other type of notification and/or alarm. In some embodiments, anaverage velocity (e.g., of the person or other load bearing structure)can be measured during a window of time and the rule can be based on,for example, a number of velocity emission states out of a number oftime period (e.g., whether there are more than 100 high-velocityemission states out of 150 time periods, etc.)

FIG. 10 illustrates an exemplary punctuated equilibrium during quiescentstanding based on pressure points acquired from a sensor (e.g., sensorfrom the shoe, walker, prosthetic leg, walking aid, portion of a roboticdevice or other load bearing structure as described herein), accordingto an illustrative embodiment. In this example, the sensor was locatedon a shoe worn by a person/subject (i.e., person) that stood for twominutes on an EquiTest platform (available from NeuroCom Internationalof Clackamas, Oreg.). Pressure was sampled at the right toe, the rightheel, the left toe, and the left heel, at a rate of one hundred Hz. Thesensor data was classified by the HMM. Center-of-force from left foot(0.0) to right foot (1.0) is shown on the x-axis. Center-of-force in theanterior (1.0) to posterior (0.0) direction is plotted on the y-axis,normalized by foot length. The subject's center-of-force was initiallydynamic 1001 (e.g., as indicated by light shading), but settled into aninitial equilibrium 1005 (e.g., as indicated by lighter shading). Adynamic escape trajectory 1010 (e.g., as indicated by lighter shadingwith light border) led to settling in a second equilibrium 1015 (e.g.,as indicated by medium shading). A dynamic reversion trajectory 1020(e.g., as indicated by medium shading with dark border) did not disturbthe equilibrium, but a subsequent escape trajectory 1025 (e.g., asindicated by medium shading with light border) led the subject to settleinto a final equilibrium 1030 (e.g., as indicated by dark shading).Towards the end of the two minutes, the subject entered a dynamictrajectory 1035 (e.g., as indicated by dark shading with light border).In FIG. 10, pentagons were used to mark the center-of-force of eachequilibrium. The pentagon size can correspond to dwell time. The lengthof the embedded line can be the average distance of each point in theequilibrium from the equilibrium center-of-mass, a measure of thecompactness of the equilibrium. In some embodiments, the center-of-forceinformation is utilized for the range of postural stability. Forexample, the range of pressures collected from the subject while thesubject is standing is used to create the range of postural stability.The range of postural stability can be utilized to determine the nextpostural state.

FIG. 11 illustrates an exemplary safe zone based on pressure pointsacquired from a sensor (e.g., sensor from the shoe, walker, prostheticleg, walking aid, portion of a robotic device or other load bearingstructure as described herein), according to an illustrative embodiment.In this example, the sensor was located on a shoe worn by aperson/subject. Eighteen subjects stood for two intervals of two minuteseach on an EquiTest platform. Weight distribution from left foot (0.0)to right foot (1.0) is shown on the x-axis. Center-of-force in theanterior (1.0) to posterior (0.0) direction is plotted on the y-axis,normalized by foot length. The different shadings correspond todifferent individuals. In FIG. 11, the safe zone 1105 is elliptical inshape, but is far larger than the region encompassed by a singleequilibrium or any two minute standing interval. The range of pressurescollected from the eighteen subjects can be utilized to create apredetermined range of postural stability. For example, people with thesame characteristics (e.g., age, height, weight) as the eighteensubjects receive the predetermined range of postural stabilitypreprogrammed into their stability processing module.

FIG. 12 illustrates an exemplary punctuated equilibrium during quiescentstanding with eyes closed based on pressure points acquired from asensor (e.g., sensor from the shoe, walker, prosthetic leg, walking aid,portion of a robotic device or other load bearing structure as describedherein), according to an illustrative embodiment. In this example, thesensor was located on a shoe worn by a person/subject. The subject stoodfor two minutes on an EquiTest platform. Pressure was sampled at theright toe, the right heel, the left toe, and the left heel, at a rate ofone hundred Hz. The sensor data was classified by the HMM.Center-of-force from left foot (0.0) to right foot (1.0) is shown on thex-axis. Center-of-force in the anterior (1.0) to posterior (0.0)direction was plotted on the y-axis, normalized by foot length. Earlierto later equilibria 1201, 1205, 1210, 1215, 1220 and 1225 areillustrated by the different shadings/regions (i.e., as indicated bylighter shading to dark shading). Pentagons were used to mark thecenter-of-force of each equilibrium. Pentagon size can correspond todwell time. The length of the embedded line can be the average distanceof each point in the equilibrium from the equilibrium center-of-mass, ameasure of the compactness of the equilibrium. Dynamic trajectories(e.g., trajectories 1230A-E) that can revert to the equilibrium arebordered in a dark border. Trajectories which lead to escape from theequilibrium (e.g., trajectories 1235A-B) are bordered by a light border.In this case, the subject in this example oscillated through sixequilibria 1201, 1205, 1210, 1215, 1220 and 1225 in two minutes.

FIG. 13 illustrates an exemplary punctuated equilibrium during quiescentstanding with eyes open based on pressure points acquired from a sensor(e.g., sensor from the shoe, walker, prosthetic leg, walking aid,portion of a robotic device or other load bearing structure as describedherein), according to an illustrative embodiment. In this example, thesensor was located on a shoe worn by a person/subject. The same subjectas in FIG. 12 stood for two minutes on an EquiTest platform, this timewith eyes open. Pressure was sampled at the right toe, the right heel,the left toe, and the left heel, at a rate of one hundred Hz. The sensordata was classified by the HMM. Center-of-force from left foot (0.0) toright foot (1.0) is shown on the x-axis. Center-of-force in the anterior(1.0) to posterior (0.0) direction is plotted on the y-axis, normalizedby foot length. A single equilibrium 1301 is seen. A dynamic trajectory1305 (e.g., as indicated by dark border) roughly midway through theprotocol ultimately reverts to the equilibrium. When eyes are open,visual feedback can enable greater stability.

FIG. 14 illustrates an exemplary punctuated equilibrium during perturbedstanding based on pressure points acquired from a sensor (e.g., sensorfrom the shoe, walker, prosthetic leg, walking aid, portion of a roboticdevice or other load bearing structure as described herein), accordingto an illustrative embodiment. In this example, the sensor was locatedon a shoe worn by a person/subject. The subject stood for two minutes onan EquiTest platform, squatting after forty seconds and lifting armsafter eighty seconds. Pressure was sampled at the right toe, the rightheel, the left toe, and the left heel, at a rate of one hundred Hz. Thesensor data was classified by a Hidden Markov Model. Center-of-forcefrom left foot (0.0) to right foot (1.0) is shown on the x-axis.Center-of-force in the anterior (1.0) to posterior (0.0) direction isplotted on the y-axis, normalized by foot length. The subject entered aninitial equilibrium 1401 for forty seconds (e.g., as indicated by thelighter shading and pentagon with lighter shading), which was abruptlyinterrupted by a squat 1405 (e.g., as indicated by lighter shading withlight border). This led into a new equilibrium 1410 (e.g., as indicatedby dark shading and pentagon with dark shading). The arm lift 1415(e.g., as indicated by dark shading with dark border) did not lead todeterioration of the equilibrium. The HMM was able to effectivelyidentify both the squat and the arm-lift.

In some embodiments, the range of postural stability is determined byquantifying the dwell time, the size, the shape of the equilibrium, thedynamic trajectories, and/or the zone of stable equilibrium. FIGS. 10through 14 illustrate exemplary dwell time, size, shape of equilibrium,dynamic trajectories, and zone of stable equilibrium that can beutilized to determine the range of postural stability of asubject/person.

Although the figures and examples described herein illustrate people androbots, the input sensors (e.g., 120) can be coupled to any type of loadbearing structure associated with a structure. Based on the sensorinformation, the postural states can be determined for the structure.The structure can be, for example, a vehicle, a person, a robot, and/orany other type of structure associated with a load bearing structure.The load bearing structure can be, for example, a leg, a walking aid, anaxle couple to one or more wheels, and/or any other type of structurethat is load bearing.

FIG. 15A illustrates data acquired from at least one pressure sensorover time, according to an illustrative embodiment. FIG. 15B illustratesa method for determining postural stability of a person based on thedata acquired from the pressure sensor, according to an illustrativeembodiment of the invention. Pressure data points 1501-1512 can beacquired from a sensor (e.g., sensor/input sensor from a shoe worn bythe person, a walker, a prosthetic leg, walking aid, portion of arobotic device or other load bearing structure as described herein). Thex-axis and the y-axis can correspond to locations of the pressure datapoints. For example, in the case where a pressure sensor is located on ashoe worn by a person/subject, then the x-axis can represent the laterallocations (e.g., right and left foot) on the person and the y-axis canrepresent fore-aft locations (e.g., toe and heel) on the person's foot.

A method for determining postural stability (e.g., of a person or arobot or vehicle, etc.) can include acquiring a plurality of pressuredata points (e.g., pressure data points 1501-1512) over a period of time(e.g., at time t_i, t_i+1 . . . t_i+n) from at least one pressure sensor(step 1520). A postural state can be identified for each pressure datapoint to generate a plurality of postural states (e.g., which caninclude a range of postural states) (step 1525). A postural state of aperson (e.g., or other load bearing structure) at a point in time can bedetermined based on at least the plurality of postural states (step1530). In some embodiments, the postural state a point in time is thesubsequent postural state of the person (e.g., at time t_i+n+1). In someembodiments, the postural state of the person is determined at a laterpoint in time, t_i+n+m where m is a value corresponding to how much timehas lapsed between when the pressure data points were acquired to whenthe data points are analyzed to determine the postural state. In someembodiments, a person can be identified as posturally stable or unstablebased on the data points/postural states (step 1535).

A method for determining postural stability (e.g., stability of aperson) can also include the step of acquiring at least a first pressuredata point and a second pressure data point from at least one pressuresensor (e.g., sensor/input sensor from the shoe, walker, prosthetic leg,walking aid, portion of a robotic device or other load bearing structureas described herein). The first pressure data point can correspond topressure data point 1501 and the second pressure data point can be 1502.The method can also include identifying a first postural state and asecond postural state based on the first and second pressure data points1501 and 1502. The first and second postural states can be individuallyidentified as static or dynamic. The method can also include determininga postural state (e.g., of the person) at a point in time (e.g., asubsequent postural state or a postural state of a person at a laterpoint in time after the data points have been acquired) based on atleast the first postural state and the second postural state. Forexample, person can be identified as posturally stable or unstable basedon at least the identified first postural state and the identifiedsecond postural state (e.g., step 1535).

The step (e.g., step 1525 from FIG. 15B) of identifying/determining apostural state for each corresponding pressure data point (e.g., any oneof pressure data points 1501-1512 from FIG. 15A) can generate aplurality of postural states. A range of postural stability can bedetermined based on the plurality of pressure data points or thecorresponding plurality of postural states (e.g. from step 1525 fromFIG. 15B).

In some embodiments, the postural state identified for each pressuredata points (e.g., any one of pressure data points 1501-1512 from FIG.15A) can be identified as a static postural state or a dynamic posturalstate. A dynamic postural state can be defined as when the person ismoving between static postural states (e.g., moving from a first staticpostural state to a second static postural state). By way of example, inFIG. 15A, pressure data points 1503-1506 and pressure data points1509-1512 can be each identified as static postural states and pressuredata points 1501-1502 and pressure data points 1507-1508 can be eachidentified as dynamic postural states.

Threshold functions can be performed on the pressure data points (e.g.,pressure data points 1501-1512 from FIG. 15A). A person can bedetermined or identified as being posturally stable or posturallyunstable (e.g., step 1535 from FIG. 15B) based on a number of times theperson is in the dynamic postural state. For example, if the number oftimes the person transitions from a static postural state to a dynamicpostural state exceeds a predetermined threshold, then the person can beidentified as being posturally unstable.

In some embodiments, data points (e.g., pressure data points 1501-1512from FIG. 15A) from input sensors (e.g., pressure sensors) can beprocessed to determine if a condition occurs. For example, a rule caninclude measuring an average velocity (e.g., of a person or other loadbearing structure) over a period/window of time. In some embodiments,the rule or condition used to assess/determine postural stability can bebased on the number of velocity related emission states out of a numberof points/periods of time (e.g., whether there are more than 100high-velocity emission states out of 150 time periods, etc). In someembodiments, the rules based methods do not use the number of dynamicstates to determine a person's overall postural state (e.g., determiningthe postural stability). A simple thresholding algorithm can take theoutputs themselves, (e.g., such as the velocity of a person) in either abinned form (e.g., in the emission matrix) or unbinned form.

In some embodiments, the plurality of postural states based on thepressure data points 1501-1512 follows a punctuated equilibrium (e.g.,as described in FIG. 14). A continuous series of static postural statescan define an equilibrium. By way of example, each of the plurality ofpressure data points 1501-1512 can correspond to the followingexemplary/illustrative sequence of postural states, where “S”corresponds to a static postural state and “D” corresponds to a dynamicpostural state: D (e.g., data point 1501), D (e.g., data point 1502), S(e.g., data point 1503), S (e.g., data point 1504), S (e.g., data point1505), S (e.g., data point 1506), D (e.g., data point 1507), D (e.g.,data point 1508), S (e.g., data point 1509), S (e.g., data point 1510),S (e.g., data point 1511), S (e.g., data point 1512), etc. A series ofstatic postural states (e.g., and corresponding pressure data points1503-1506 and 1509-1512) can be grouped together to define anequilibrium 1513 or 1514. A person can be determined/identified as beingposturally stable or posturally unstable (e.g., step 1535 from FIG. 15B)based on a number of distinct equilibria 1513 and 1514. By way ofexample, if the number of distinct equilibria (e.g., the number ofseries/groups of static postural states) exceeds a certain threshold,then the person can be identified as being posturally unstable.

The “postural state of the person at a point in time” (e.g., step 1530from FIG. 15B) can be defined, for example, as a past postural state ofthe person, a current postural state of the person or a subsequentpostural state of the person. Any one of a past postural state, currentpostural state or a subsequent postural state of the person can bedetermined by looking at or analyzing the plurality of pressure datapoints 1501-1512 acquired from pressure sensor.

In some embodiments, the postural state of the person at a point in time(e.g., step 1530 from FIG. 15B) can be calculated or determined in realtime as the pressure data points 1501-1512 are being acquired by asensor. Pressure data points 1501-1512 can represent a postural state ofthe position at each point in time during an acquisition period (e.g.,step 1520 from FIG. 15B). For example, pressure data points 1501-1512can represent pressure data points at times t_1, t_2, t_3 . . . t_12,where points of time t_1 through t_12 can define a data acquisitionperiod of time. If the postural state of a person at a point in time iscalculated in real time, pressure data points 1501-1512 can be acquiredand a postural state of the person at time t_13, etc. (e.g., asubsequent postural state), etc. can be calculated.

In some embodiments, pressure data points 1501-1512 can be acquired overa period of time and later used to calculate a postural state of theperson at a later point in time (e.g., 6 months later). For example, asstated above, pressure data points 1501-1512 from FIG. 15A can representpressure data points at times t_1, t_2, t_3 . . . t_12. By way ofexample, pressure data points 1501-1512 can later be used for diagnosticpurposes or analyzed at a later point in time to project what thecurrent postural state of the person is and what a subsequent posturalstate of the person might be. For example, pressure data points1501-1512 acquired during times t_1, t_2, t_3 . . . t_12 can be used ata later point in time (e.g., time point t_50) to determine a currentpostural state (e.g., at time point t_50), a previous postural state(e.g., at time point t_49) or subsequent postural state (e.g., at timepoint t_51).

In some embodiments, a Hidden Markov Model can be used to calculate apostural state of the person (e.g., step 1530 from FIG. 15B) at a pointin time (e.g., a past postural state, current postural state orsubsequent postural state of the person). For example, pressure datapoints (e.g., data points 1501-1512 from FIG. 15A) can be used in aHidden Markov Model to calculate a subsequent postural state of aperson. Each pressure data point 1501-1512 can be identified as type ofpostural state (e.g., static postural state or dynamic postural state)(e.g., step 1525 from FIG. 15B). Pressure data points 1501-1512 can beacquired over a period of time (e.g., acquisition period at time pointst_1, t_2, t_3 . . . t_12, for example, in step 1520 in FIG. 15B). Therange of postural states (e.g., the sequence of postural statesgenerated from pressure data points 1501-1512), a probability oftransitioning between the types of postural states (e.g., probability oftransitioning between static state or dynamic state) and a currentpostural sate (e.g., postural state at time point t_12) can be used in aHidden Markov Model to determine a subsequent postural state (e.g., apostural state at time point t_13). A Hidden Markov Model can be used todetermine a past or a present postural state (e.g., a postural state attime point t_11 or t_12).

In some embodiments, a Bayesian segmentation can be applied to theplurality of pressure data points (e.g., corresponding to pressure datapoints 1501-1512 from FIG. 15A and determined, for example, at step 1525from FIG. 15B) to determine a postural state of a person at a point intime.

In some embodiments, the postural state of the person at a point in time(e.g., a subsequent postural state of the person) (step 1530) isdetermined or calculated based on at least probability of transitioningbetween the static postural state and the dynamic postural state. Theprobability of transitioning between states can be calculated based onthe plurality of postural states of the person over the period of time(e.g., over an acquisition period). For example, the probability oftransitioning between states can be determined by looking at thepostural states that correspond to the pressure data points 1501-1512that were acquired from the sensor (e.g., by looking at how the sequenceof the postural states varies over time) in step 1530 of FIG. 15B. Byway of example, if pressure data points 1501-1512 correspond to thefollowing sequence of postural states: D, D, S, S, S, S, D, D, S, S, S,S where “D” is a dynamic state and “S” is a static state, then anyportion or all of the sequence can be used to calculate/determine aprobability of transitioning between the static postural state and thedynamic postural state.

In some embodiments, each of the plurality of pressure data points1501-1512 from FIG. 15A reflects a location of a center of mass of theperson at a point in time. For example, if the sensor is on a shoe wornby a person, as the person is shifting/moving/swaying, the center ofmass/gravity/force of the person can move as well. In some embodiments,a change of the location of the center of mass of the person over theperiod of time is determined based on the plurality of pressure datapoints 1501-1512. A selected set of the plurality of pressure datapoints can be grouped (e.g., groups 1513 or 1514) as corresponding to astatic or dynamic postural state of the person. The selected set of theplurality of pressure data points can be grouped based on thecorresponding location of center of mass/gravity/force. For example,pressure data points 1503-1506 and 1509-1912 can indicate that theperson's center of mass/gravity/force has remained relatively stable(e.g., a static postural state) because the location of the center ofmass has not changed relative to the other data points. However,pressure data points 1501, 1502, 1507 and 1508 can indicate that thelocation of the person's center of mass/gravity/force is changing (e.g.,a dynamic postural state).

In some embodiments, a person's activity can be monitored in addition toacquiring pressure sensor information. For example, an acceleration of aperson over time can be acquired (e.g., by use of a measurement devicesuch as an accelerometer). In some embodiments, a location of the personcan also be acquired (e.g., by using a GPS device). Informationregarding a person's activity can be used, for example, for diagnosticpurposes.

In some embodiments, the pressure data points 1501-1512 (e.g.,information acquired from pressure sensors on a load bearing structureas described herein) can be analyzed using, for example, Fouriertransform or other signal processing techniques. In some embodiments,the pressure data points can be analyzed to determine if there is aperiodicity to the data (e.g., pressure data points 1501-1512 from FIG.15A). A periodicity of the pressure data points can be used inconnection with biometric data (e.g., heart rate, breathing, etc.) andcan be used for diagnostic purposes.

The above-described embodiments, methods and systems can also be used inconnection with feedback (e.g., immediate feedback). By way of example,feedback (e.g., real-time or immediate) can be used to alert a person ora load bearing structure as described herein (e.g., robot, vehicle,etc.) The feedback could be used in connection with the determinedpostural stability (e.g., to alert the person that they are or will beposturally unstable) (e.g., step 1540 of FIG. 15B). By way of example,feedback could be accomplished via vibration (e.g., stimulation orvibration at feet or elsewhere), via vision (e.g., presenting thepressure data to the person's visual field, either on a phone, specialglasses, or modified contact lenses), via hearing (e.g., head-phones orbone-phones) via taste (using some sort of cartridge in the person'smouth), via temperature (e.g., at feet or elsewhere), via other sensorymechanisms (e.g., a tactile mechanism), or any combination thereof. Thisfeedback could be used to improve balance or in connection with balancetraining or physical therapy, etc.

FIG. 16 is an illustrative graph comparing the number of equilibria 1600from subjects. Data (e.g., such as data described above in FIGS. 10-14taken from input sensors) taken from subjects with their eyes open 1605and subjects with their eyes closed 1610. As shown in the graph, thenumber of equilibria (e.g., equilibria as described above in FIGS.10-14) increased when the subjects had their eyes closed (i.e. whichcould lead to imbalance/postural instability) as compared to when thesubjects had their eyes opened.

The above-described systems and methods can be implemented in digitalelectronic circuitry, in computer hardware, firmware, and/or software.The implementation can be as a computer program product. Theimplementation can, for example, be in a machine-readable storagedevice, for execution by, or to control the operation of, dataprocessing apparatus. The implementation can, for example, be aprogrammable processor, a computer, and/or multiple computers.

A computer program can be written in any form of programming language,including compiled and/or interpreted languages, and the computerprogram can be deployed in any form, including as a stand-alone programor as a subroutine, element, and/or other unit suitable for use in acomputing environment. A computer program can be deployed to be executedon one computer or on multiple computers at one site.

Method steps can be performed by one or more programmable processorsexecuting a computer program to perform functions of the invention byoperating on input data and generating output. Method steps can also beperformed by and an apparatus can be implemented as special purposelogic circuitry. The circuitry can, for example, be a FPGA (fieldprogrammable gate array) and/or an ASIC (application specific integratedcircuit). Modules, subroutines, and software agents can refer toportions of the computer program, the processor, the special circuitry,software, and/or hardware that implements that functionality.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor receives instructions and data from a read-only memory or arandom access memory or both. The essential elements of a computer are aprocessor for executing instructions and one or more memory devices forstoring instructions and data. Generally, a computer can include, can beoperatively coupled to receive data from and/or transfer data to one ormore mass storage devices for storing data (e.g., magnetic,magneto-optical disks, or optical disks).

Data transmission and instructions can also occur over a communicationsnetwork. Information carriers suitable for embodying computer programinstructions and data include all forms of non-volatile memory,including by way of example semiconductor memory devices. Theinformation carriers can, for example, be EPROM, EEPROM, flash memorydevices, magnetic disks, internal hard disks, removable disks,magneto-optical disks, CD-ROM, and/or DVD-ROM disks. The processor andthe memory can be supplemented by, and/or incorporated in specialpurpose logic circuitry.

To provide for interaction with a user, the above described techniquescan be implemented on a computer having a display device. The displaydevice can, for example, be a cathode ray tube (CRT) and/or a liquidcrystal display (LCD) monitor. The interaction with a user can, forexample, be a display of information to the user and a keyboard and apointing device (e.g., a mouse or a trackball) by which the user canprovide input to the computer (e.g., interact with a user interfaceelement). Other kinds of devices can be used to provide for interactionwith a user. Other devices can, for example, be feedback provided to theuser in any form of sensory feedback (e.g., visual feedback, auditoryfeedback, or tactile feedback). Input from the user can, for example, bereceived in any form, including acoustic, speech, and/or tactile input.

The above described techniques can be implemented in a distributedcomputing system that includes a back-end component. The back-endcomponent can, for example, be a data server, a middleware component,and/or an application server. The above described techniques can beimplemented in a distributing computing system that includes a front-endcomponent. The front-end component can, for example, be a clientcomputer having a graphical user interface, a Web browser through whicha user can interact with an example implementation, and/or othergraphical user interfaces for a transmitting device. The components ofthe system can be interconnected by any form or medium of digital datacommunication (e.g., a communication network). Examples of communicationnetworks include a local area network (LAN), a wide area network (WAN),the Internet, wired networks, wireless networks, a packet-based network,and/or a circuit-based network.

Packet-based networks can include, for example, the Internet, a carrierinternet protocol (IP) network (e.g., local area network (LAN), widearea network (WAN), campus area network (CAN), metropolitan area network(MAN), home area network (HAN)), a private IP network, an IP privatebranch exchange (IPBX), a wireless network (e.g., radio access network(RAN), 802.11 network, 802.16 network, general packet radio service(GPRS) network, HiperLAN), and/or other packet-based networks.Circuit-based networks can include, for example, the public switchedtelephone network (PSTN), a private branch exchange (PBX), a wirelessnetwork (e.g., RAN, bluetooth, code-division multiple access (CDMA)network, time division multiple access (TDMA) network, global system formobile communications (GSM) network), and/or other circuit-basednetworks.

The system can include clients and servers. A client and a server aregenerally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

The handheld device can include, for example, a computer, a computerwith a browser device, a telephone, an IP phone, a mobile device (e.g.,cellular phone, personal digital assistant (PDA) device, laptopcomputer, electronic mail device), and/or other communication devices.The browser device includes, for example, a computer (e.g., desktopcomputer, laptop computer) with a world wide web browser (e.g.,Microsoft® Internet Explorer® available from Microsoft Corporation,Mozilla® Firefox available from Mozilla Corporation). The mobilecomputing device includes, for example, a personal digital assistant(PDA).

Comprise, include, and/or plural forms of each are open ended andinclude the listed parts and can include additional parts that are notlisted. And/or is open ended and includes one or more of the listedparts and combinations of the listed parts.

While this invention has been particularly shown and described withreferences to preferred embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

1. A computer-implemented method for determining postural stability of aperson, the method comprising: acquiring, by a processor, a plurality ofpressure data points over a period of time from at least one pressuresensor; identifying, by the processor, a postural state for eachpressure data point to generate a plurality of postural states; anddetermining, by the processor, a postural state of the person at a pointin time based on at least the plurality of postural states and aprobability of transitioning between at least one of the plurality ofpostural states and another postural state.
 2. The method of claim 1,further comprising acquiring, by the processor, at least one of anacceleration or a location of the person over the period of time.
 3. Themethod of claim 1, further comprising determining, by the processor, arange of postural stability states based on the plurality of pressuredata points.
 4. The method of claim 1, wherein determining comprisesusing a machine learning technique on the plurality of pressure datapoints to generate the plurality of postural states.
 5. The method ofclaim 4, wherein determining comprises using a Hidden Markov Model onthe plurality of pressure data points or applying Bayesian segmentationto the plurality of pressure data points to generate the plurality ofpostural states.
 6. The method of claim 1, wherein acquiring comprisesacquiring a plurality of pressure data points from at least one pressuresensor on at least one of a shoe, a sock, a sole insert, a cane, acrutch, a walker, a walking aid, a prosthetic leg, a robotic leg, avehicle, or an axle connected to at least one wheel.
 7. The method ofclaim 1, wherein each of the plurality of pressure data points reflectsa location of a center of mass of the person at a point in time.
 8. Themethod of claim 7, wherein identifying comprises grouping a selected setof the plurality of pressure data points as corresponding to a staticpostural state based on the location of the center of mass of the personof each of the plurality of pressure data points.
 9. The method of claim1, wherein determining comprises determining in real time, a subsequentpostural state of the person based on the plurality of pressure datapoints.
 10. The method of claim 1, wherein determining comprisesdetermining a current postural state or a subsequent postural state ofthe person based on the plurality of pressure data points, wherein theplurality of pressure data points were acquired during a previous periodin time.
 11. A computer-implemented method for determining posturalstability, the method comprising: acquiring, by a processor, at least afirst pressure data point and a second pressure data point from at leastone pressure sensor; identifying, by the processor, a first posturalstate and a second postural state based on the first and second pressuredata points; and determining, by the processor, a postural state at apoint in time based on at least the first postural state, the secondpostural state, and a probability of transitioning between at least oneof the first or second postural states and another postural state. 12.The method of claim 11, further comprising acquiring, by the processor,at least one of an acceleration or a location of a person over theperiod of time.
 13. The method of claim 11, further comprisingdetermining, by a processor, a range of postural stability states basedon the first pressure data point and the second pressure data point. 14.The method of claim 11, wherein determining comprises determining asubsequent postural state of a person.
 15. The method of claim 11,further comprising determining, by a processor, that a person isposturally stable or posturally unstable based on at least theidentified first postural state and the identified second posturalstate.
 16. The method of claim 11, wherein acquiring comprises acquiringa plurality of pressure data points from at least one pressure sensor onat least one of a shoe, a sock, a sole insert, a cane, a crutch, awalker, a walking aid, a prosthetic leg, a robotic leg, a vehicle, or anaxle connected to at least one wheel.
 17. The method of claim 11,wherein each of the first pressure data point and the second pressuredata point reflects a location of a center of mass of a person at apoint in time.
 18. The method of claim 11, wherein determining comprisesdetermining a current postural state or a subsequent postural statebased on the first pressure data point and the second pressure datapoint, wherein the first pressure data point and the second pressuredata point were acquired during a previous period in time.
 19. A systemfor determining postural stability of a person comprising: at least onepressure sensor coupled to the person that acquires a plurality ofpressure data points over a period of time; means for identifying apostural state for each pressure data point; means for generating aplurality of postural states of the person over the period of time; andmeans for determining a postural state of the person at a point in timebased on the plurality of postural states and a probability oftransitioning between at least one of the plurality of postural statesand another postural state.
 20. The system of claim 19, wherein eachpressure data point corresponds to a center of gravity of the person ata point in time.